Knowledge Graph Entity Representation and Retrieval

Recent studies indicate that more than 75% of queries issued to Web search engines aim at finding information about entities, which could be material objects or concepts that exist in the real world or fiction (e.g. people, organizations, locations, products, etc.). Most common information needs underlying this type of queries include finding a certain entity (e.g. “Einstein relativity theory”), a particular attribute or property of an entity (e.g. “Who founded Intel?”) or a list of entities satisfying a certain criteria (e.g. “Formula 1 drivers that won the Monaco Grand Prix”). These information needs can be efficiently addressed by presenting structured information about the target entity or a list of entities retrieved for these queries from a knowledge graph either directly as search results or in addition to the ranked list of documents. This course provides a summary of the latest research in knowledge graph entity representation methods and retrieval models. In the first part of this course, I will introduce different methods for entity representation: from multi-fielded documents with flat and hierarchical structure to latent dimensional representations based on tensor factorization. In the second part of this course, I will discuss recent developments in entity retrieval models, including Mixture of Language Models (MLM), Probabilistic Retrieval Model for Semi-structured Data (PRMS), Fielded Sequential Dependence Model (FSDM) and its parametric extension (PFSDM) as well as learning-to-rank methods.